Examples of buildClassifier()


Examples of org.goai.classification.impl.WekaClassifier.buildClassifier()

        try {
            // Inicialize GOAI Classifier with Weka classifier
            Classifier<double[], String> goaiClassifier = new WekaClassifier(wekaClassifier);
           
            //Process data from Map
            goaiClassifier.buildClassifier(makeMapOutOfInstances(super.getProblemSrc()));
           
            return goaiClassifier;
        } catch (Exception ex) {
            Logger.getLogger(WekaClassifierEvaluation.class.getName()).log(Level.SEVERE, null, ex);
        }
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Examples of weka.classifiers.Classifier.buildClassifier()

    try {
      current = AbstractClassifier.makeCopy(template);
    } catch (Exception ex) {
      m_Log.logMessage("Problem copying classifier: " + ex.getMessage());
    }
    current.buildClassifier(train);
    Instances test = inst.testCV(numFolds, fold);
    m_Log.statusMessage("Evaluating model for fold "
            + (fold + 1) + "...");
    for (int jj=0;jj<test.numInstances();jj++) {
      plotInstances.process(test.instance(jj), current, eval);
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Examples of weka.classifiers.Classifier.buildClassifier()

        try {
    current = AbstractClassifier.makeCopy(template);
        } catch (Exception ex) {
    m_Log.logMessage("Problem copying classifier: " + ex.getMessage());
        }
        current.buildClassifier(train);
        eval = new Evaluation(train, costMatrix);
       
        // make adjustments if the classifier is an InputMappedClassifier
              eval = setupEval(eval, classifier, train, costMatrix,
                  plotInstances, classificationOutput, false);
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Examples of weka.classifiers.Classifier.buildClassifier()

      DenseInstance d = new DenseInstance(1.0, vals);
      simple.add(d);
    }

    simple.setClassIndex(1);
    lagFiller.buildClassifier(simple);
    System.err.println(lagFiller);
    simple = new Instances(simple, 0);

    List<Object> results = new ArrayList<Object>();
    results.add(lagFiller);
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Examples of weka.classifiers.Classifier.buildClassifier()

          // As part of learning, we also prune some of the nodes where the ratio of correctly-classified pairs to those incorrectly classified is comparable.
          // The significant advantage of not pruning is that the result is no longer sensitive to the order of elements in the tree and hence does not depend on the order in which elements have been obtained by concurrent threads.
          //final weka.classifiers.lazy.IB1 ib1 = new weka.classifiers.lazy.IB1();
          //final weka.classifiers.trees.J48 classifier = new weka.classifiers.trees.J48();
          final Classifier classifier = repTree;
          classifier.buildClassifier(dataCollector.trainingData);
          System.out.println("Entries in the classifier: "+dataCollector.trainingData.numInstances());
          System.out.println(classifier);
          dataCollector=null;// throw all the training data away.
         
          {// serialise the classifier, this is the only way to store it.
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Examples of weka.classifiers.Classifier.buildClassifier()

          // As part of learning, we also prune some of the nodes where the ratio of correctly-classified pairs to those incorrectly classified is comparable.
          // The significant advantage of not pruning is that the result is no longer sensitive to the order of elements in the tree and hence does not depend on the order in which elements have been obtained by concurrent threads.
          //final weka.classifiers.lazy.IB1 ib1 = new weka.classifiers.lazy.IB1();
          //final weka.classifiers.trees.J48 classifier = new weka.classifiers.trees.J48();
          final Classifier classifier = repTree;
          classifier.buildClassifier(dataCollector.trainingData);
          System.out.println("Entries in the classifier: "+dataCollector.trainingData.numInstances());
          System.out.println(classifier);
          dataCollector=null;// throw all the training data away.
         
          {// serialise the classifier, this is the only way to store it.
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Examples of weka.classifiers.Classifier.buildClassifier()

          // As part of learning, we also prune some of the nodes where the ratio of correctly-classified pairs to those incorrectly classified is comparable.
          // The significant advantage of not pruning is that the result is no longer sensitive to the order of elements in the tree and hence does not depend on the order in which elements have been obtained by concurrent threads.
          //final weka.classifiers.lazy.IB1 ib1 = new weka.classifiers.lazy.IB1();
          //final weka.classifiers.trees.J48 classifier = new weka.classifiers.trees.J48();
          final Classifier classifier = repTree;
          classifier.buildClassifier(dataCollector.trainingData);
          System.out.println("Entries in the classifier: "+dataCollector.trainingData.numInstances());
          System.out.println(classifier);
          dataCollector=null;// throw all the training data away.
         
          {// serialise the classifier, this is the only way to store it.
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Examples of weka.classifiers.Classifier.buildClassifier()

          // As part of learning, we also prune some of the nodes where the ratio of correctly-classified pairs to those incorrectly classified is comparable.
          // The significant advantage of not pruning is that the result is no longer sensitive to the order of elements in the tree and hence does not depend on the order in which elements have been obtained by concurrent threads.
          //final weka.classifiers.lazy.IB1 ib1 = new weka.classifiers.lazy.IB1();
          //final weka.classifiers.trees.J48 classifier = new weka.classifiers.trees.J48();
          final Classifier classifier = repTree;
          classifier.buildClassifier(dataCollector.trainingData);
          System.out.println("Entries in the classifier: "+dataCollector.trainingData.numInstances());
          System.out.println(classifier);
          dataCollector=null;// throw all the training data away.
         
          {// serialise the classifier, this is the only way to store it.
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Examples of weka.classifiers.Classifier.buildClassifier()

        cModel=(Classifier)Util.loadObject(f);
      }
      else
      {
        cModel = (Classifier)Class.forName(this.classifier).newInstance();       
             cModel.buildClassifier(ins);
             Util.writeObject(f, cModel);
      }
      //Instantiate the classifier
        Instance base=ins.firstInstance();
      Instance sample=new Instance(base.numAttributes());
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Examples of weka.classifiers.Classifier.buildClassifier()

          // As part of learning, we also prune some of the nodes where the ratio of correctly-classified pairs to those incorrectly classified is comparable.
          // The significant advantage of not pruning is that the result is no longer sensitive to the order of elements in the tree and hence does not depend on the order in which elements have been obtained by concurrent threads.
          //final weka.classifiers.lazy.IB1 ib1 = new weka.classifiers.lazy.IB1();
          //final weka.classifiers.trees.J48 classifier = new weka.classifiers.trees.J48();
          final Classifier classifier = repTree;
          classifier.buildClassifier(dataCollector.trainingData);
          System.out.println("Entries in the classifier: "+dataCollector.trainingData.numInstances());
          System.out.println(classifier);
          dataCollector=null;// throw all the training data away.
         
          {// serialise the classifier, this is the only way to store it.
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